Kwon, S.; Kim, J.; Aymerich Armengol, R.; Lee, C.; Jung, Y. M.; Han, J.-H.; Helveg, S.; Scheu, C.; Lim, J.: Unlocking the potential of MoS2 for efficient hydrogen generation by controlling hydrothermal conditions. International Journal of Hydrogen Energy 161, 150671 (2025)
Garzón Manjón, A.; Vega-Paredes, M.; Aymerich Armengol, R.; Esteban, D.; Sanchez, S.; Bals, S.; Scheu, C.: Insights into the degradation of nanocatalysts under fuel cell conditions by 3D identical location STEM. BIO Web of Conferences 129 (2024), 13013 (2024)
Vega-Paredes, M.; Scheu, C.; Aymerich Armengol, R.: Expanding the Potential of Identical Location Scanning Transmission Electron Microscopy for Gas Evolving Reactions: Stability of Rhenium Molybdenum Disulfide Nanocatalysts for Hydrogen Evolution Reaction. ACS Applied Materials and Interfaces 15 (40), S. 46895 - 46901 (2023)
Aymerich Armengol, R.: Techniques for the assessment of the stability of (sea) water splitting nanocatalysts. Korean Institute for Energy Research, Jeju, South Korea (2023)
Vega-Paredes, M.; Aymerich Armengol, R.; Scheu, C.: Determining the degradation mechanisms and active species of electrocatalysts by identical location electron microscopy. NRF-DFG meeting “Electrodes for direct sea-water splitting and microstructure based stability analyses”, Korean Institute for Energy Research, Jeju, South Korea (2023)
Aymerich Armengol, R.: Determination of the structural and electrochemical stability of nanocatalysts for electrolyzer applications. Chemistry Department, Kangwon National University, Chuncheon-si, South Korea (2023)
Aymerich Armengol, R.: Understanding the stability of nanomaterials through electron microscopy techniques. Physics Department, Technical University of Denmark, Kongens Lyngby, Denmark (2023)
Wissenschaftler des Max-Planck-Instituts für Eisenforschung entwickeln ein neues maschinelles Lernmodell für korrosionsresistente Legierungen. Und veröffentlichen ihre Ergebnisse in der Fachzeitschrift Science Advances
Düsseldorfer Max-Planck-Wissenschaftler diskutieren den Einsatz künstlicher Intelligenz in der Materialwissenschaft und veröffentlichen Review-Artikel in der Fachzeitschrift Nature Computational Science